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Measuring the potential of individual airports for pandemic spread over the world airline network

Overview of attention for article published in BMC Infectious Diseases, February 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
11 tweeters
facebook
2 Facebook pages
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
69 Mendeley
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Title
Measuring the potential of individual airports for pandemic spread over the world airline network
Published in
BMC Infectious Diseases, February 2016
DOI 10.1186/s12879-016-1350-4
Pubmed ID
Authors

Glenn Lawyer

Abstract

Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of this early stage variation can be explained by appropriately characterizing the local network topology surrounding an outbreak's debut location. Based on a model of the WAN derived from public data, we measure for each airport the expected force of infection (AEF) which a pandemic originating at that airport would generate, assuming an epidemic process which transmits from airport to airport via scheduled commercial flights. We observe, for a subset of world airports, the minimum transmission rate at which a disease becomes pandemically competent at each airport. We also observe, for a larger subset, the time until a pandemically competent outbreak achieves pandemic status given its debut location. Observations are generated using a highly sophisticated metapopulation reaction-diffusion simulator under a disease model known to well replicate the 2009 influenza pandemic. The robustness of the AEF measure to model misspecification is examined by degrading the underlying model WAN. AEF powerfully explains pandemic risk, showing correlation of 0.90 to the transmission level needed to give a disease pandemic competence, and correlation of 0.85 to the delay until an outbreak becomes a pandemic. The AEF is robust to model misspecification. For 97 % of airports, removing 15 % of airports from the model changes their AEF metric by less than 1 %. Appropriately summarizing the size, shape, and diversity of an airport's local neighborhood in the WAN accurately explains much of the macro-level stochasticity in pandemic outcomes.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 1%
Switzerland 1 1%
Brazil 1 1%
Unknown 66 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 20%
Researcher 11 16%
Student > Bachelor 10 14%
Student > Ph. D. Student 9 13%
Student > Doctoral Student 6 9%
Other 7 10%
Unknown 12 17%
Readers by discipline Count As %
Medicine and Dentistry 9 13%
Agricultural and Biological Sciences 9 13%
Nursing and Health Professions 8 12%
Physics and Astronomy 4 6%
Environmental Science 3 4%
Other 24 35%
Unknown 12 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 May 2020.
All research outputs
#944,052
of 17,704,521 outputs
Outputs from BMC Infectious Diseases
#170
of 6,245 outputs
Outputs of similar age
#15,604
of 236,392 outputs
Outputs of similar age from BMC Infectious Diseases
#1
of 1 outputs
Altmetric has tracked 17,704,521 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,245 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 236,392 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them